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A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis
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Kang, Jian, Nichols, Thomas E., Wager, Tor D. and Johnson, Timothy D. (2014) A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis. The Annals of Applied Statistics, Volume 8 (Number 3). pp. 1800-1824. doi:10.1214/14-AOAS757 ISSN 1932-6157.
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Official URL: http://dx.doi.org/10.1214/14-AOAS757
Abstract
Neuroimaging meta-analysis is an important tool for finding consistent effects over studies that each usually have 20 or fewer subjects. Interest in meta-analysis in brain mapping is also driven by a recent focus on so-called “reverse inference”: where as traditional “forward inference” identifies the regions of the brain involved in a task, a reverse inference identifies the cognitive processes that a task engages. Such reverse inferences, however, require a set of meta-analysis, one for each possible cognitive domain. However, existing methods for neuroimaging meta-analysis have significant limitations. Commonly used methods for neuroimaging meta-analysis are not model based, do not provide interpretable parameter estimates, and only produce null hypothesis inferences; further, they are generally designed for a single group of studies and cannot produce reverse inferences. In this work we address these limitations by adopting a nonparametric Bayesian approach for meta-analysis data from multiple classes or types of studies. In particular, foci from each type of study are modeled as a cluster process driven by a random intensity function that is modeled as a kernel convolution of a gamma random field. The type-specific gamma random fields are linked and modeled as a realization of a common gamma random field, shared by all types, that induces correlation between study types and mimics the behavior of a univariate mixed effects model. We illustrate our model on simulation studies and a meta-analysis of five emotions from 219 studies and check model fit by a posterior predictive assessment. In addition, we implement reverse inference by using the model to predict study type from a newly presented study. We evaluate this predictive performance via leave-one-out cross-validation that is efficiently implemented using importance sampling techniques.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
| Journal or Publication Title: | The Annals of Applied Statistics | ||||||
| Publisher: | Institute of Mathematical Statistics | ||||||
| ISSN: | 1932-6157 | ||||||
| Official Date: | 23 October 2014 | ||||||
| Dates: |
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| Volume: | Volume 8 | ||||||
| Number: | Number 3 | ||||||
| Page Range: | pp. 1800-1824 | ||||||
| DOI: | 10.1214/14-AOAS757 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access |
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